Making change stick
These insights come from my work inside organizations navigating change, from AI adoption to broader shifts in how teams operate and build capability. Through conversations with employees, teams, and leaders, I’ve seen consistent patterns in what enables progress and what slows it down.
The articles and frameworks on this page capture those patterns and offer practical ways to assess, measure, and sustain change over time.
THE REALITY CHECK
What AI adoption actually looks like inside organizations
AI adoption is not a curve, it is a patchwork
AI adoption rarely moves evenly. Confident users often sit alongside unchanged workflows and cautious peers, creating the appearance of progress without a shared organizational baseline.
ADOPTION | DISTRIBUTION
Technical fluency as the strongest predictor of AI adoption
Adoption tends to follow comfort with digital tools and ambiguity. Age, title, and tenure matter far less than hands-on fluency and willingness to experiment.
CAPABILITY | SIGNAL
Training is not the bottleneck, workflows are
Learning about AI is rarely enough on its own. Adoption becomes reliable when AI is woven into how work already gets done.
WORKFLOWS | ENABLEMENT
PRACTICE & IMPACT
IMPACT | ROI
